CSC 411 - Introduction to Artificial Intelligence

Catalog Description:

Overview and definitions of Artificial Intelligence (AI). Search, including depth-first and breadth-first techniques with backtracking. Knowledge representation with emphasis on logical methods, Horn databases, resolution, quantification, unification, skolemization and control issues; non-monotonic reasoning; frames; semantic nets. AI systems, including planning, learning, natural language and expert systems. An AI programming language may be taught at the instructor's discretion.

Contact Hours: Prerequisites: CSC316 or ECE309
Co-requisites: None
Restrictions: None
Coordinator: Dr. Collin Lynch
Textbook: Artificial Intelligence: A Modern Approa

Course Outcomes:

At the end of this course students will be able to:
  1. Identify representations and methodologies useful in the development of computer-based systems which exhibit aspects of intelligent behavior;
  2. Program simple intelligent agents to operate in simple environments;
  3. Identify the utility and limitations of knowledge representation methodologies such as propositional and predicate logic, rule-based systems, and probabilistic systems;
  4. Identify the utility and limitations of companion reasoning methods, including resolution, rule processing, probabilistic reasoning, machine learning, and natural language processing;
  5. Distinguish various uninformed and informed search algorithms and identify when each is appropriate;
  6. Read and write simple logic programs using a high-level AI language such as Prolog or Lisp;
  7. Design and implement a series of simple intelligent agents of increasing complexity.


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